Optimal selection of diffusion-weighting gradient waveforms using compressed sensing and dictionary learning

Raphaël Truffet 1 Christian Barillot 1 Emmanuel Caruyer 1
1 Empenn
INSERM - Institut National de la Santé et de la Recherche Médicale, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Acquisition sequences in diffusion MRI rely on the use time-dependent magnetic field gradients. Each gradient waveform encodes a diffusion-weighted measure; a large number of such measurements are necessary for the in vivo reconstruction of microstructure parameters. We propose here a method to select only a subset of the measurements while being able to predict the unseen data using compressed sensing. We learn a dictionary using a training dataset generated with Monte-Carlo simulations; we then compare two different heuristics to select the measures to use for the prediction. We found that an undersampling strategy limiting the redundancy of the measures allows for a more accurate reconstruction when compared with random undersampling with similar sampling rate.
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Raphaël Truffet, Christian Barillot, Emmanuel Caruyer. Optimal selection of diffusion-weighting gradient waveforms using compressed sensing and dictionary learning. ISMRM 2019 - 27th Annual Meeting & Exhibition, May 2019, Montréal, Canada. pp.1-3. ⟨inserm-02015394v2⟩

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